More detailed information about PeriCoDe/SALIC can be found in:

E. Chatzilari, S. Nikolopoulos, Y. Kompatsiaris and J. Kittler, “SALIC: Social Active Learning for Image Classification,” in IEEE Transactions on Multimedia, vol. 18, no. 8, pp. 1488-1503, Aug. 2016. (URL: http://dx.doi.org/10.1109/TMM.2016.2565440)
Maronidis, A. et al. (2016). PERICLES Deliverable D4.3: Content Semantics and Use Context Analysis Techniques. http://pericles-project.eu/uploads/files/PERICLES_WP4_D4_3_Content_Semantics_and_Use_Context_Analysis_Techniques_V1.pdf

Although PeriCoDe addresses a very important problem in automatic image annotation, the collection of annotated training data is only one aspect of the process: the other part is the training of a classifier using these data in order to annotate – or classify – unseen images. This is known as machine learning and is performed in the test implementation of SALIC’s wrapper.m – to learn more about this, the interested reader should explore the following (non exhaustive list of) resources:

https://www.coursera.org/learn/machine-learning

https://darshanhegde.wordpress.com/2014/08/19/learn-machine-learning-the-hard-way/